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Record W7133080567

The Relationship between Large-scale Atmospheric Oscillations and Cyclone Patterns, and Their Impacts on Summer Precipitation Distribution in the Canadian Arctic

2022· dissertation· W7133080567 on OpenAlexaboutno aff
Xiaomeng Zuo

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPrecipitationSnowArcticCyclone (programming language)Arctic geoengineeringNorth Atlantic oscillationArctic dipole anomalySea iceThe arctic
DOInot available

Abstract

fetched live from OpenAlex

A trend analysis of the Arctic System Reanalysis Version 2 (ASRv2) data from 2000 to 2015 showed that changes occurring with summer rainfall and snowfall are synchronized in the Canadian Arctic. The precipitation distribution in the Canadian Arctic with the low-pressure system (center pressure < 1000hPa) patterns showed high consistency; however, snowfall in areas including Ellesmere Island and northeastern mainland NU was not found to be related to the cyclone distribution. The influences of the summer Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) on the cyclones vary regionally, leading to a complicated distribution pattern of rainfall and snowfall. These variations might also be related to sea ice opening and local evaporation, in addition to the moving jet stream. Under the circumstances of global warming and Arctic amplification, changes in summer precipitation might have substantial impacts on the hydrology, sea ice, and permafrost conditions in the Canadian Arctic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.275
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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